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An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties

arXiv 2005.07609 ↗First posted 15 May 2020physics.comp-ph cond-mat.mtrl-sci cs.LGcited by 53

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Abstract (arXiv)

Realizing general inverse design could greatly accelerate the discovery of new materials with user-defined properties. However, state-of-the-art generative models tend to be limited to a specific composition or crystal structure. Herein, we present a framework capable of general inverse design (not limited to a given set of elements or crystal structures), featuring a generalized invertible representation that encodes crystals in both real and reciprocal space, and a property-structured latent space from a variational autoencoder (VAE). In three design cases, the framework generates 142 new crystals with user-defined formation energies, bandgap, thermoelectric (TE) power factor, and combinations thereof. These generated crystals, absent in the training database, are validated by first-principles calculations. The success rates (number of first-principles-validated target-satisfying crystals/number of designed crystals) ranges between 7.1% and 38.9%. These results represent a significant step toward property-driven general inverse design using generative models, although practical challenges remain when coupled with experimental synthesis.

Key facts. Cited by 53 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 May 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 11; 2024: 13; 2025: 15; 2026: 9.

53 papers.

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  1. 2023Crystal Structure Prediction by Joint Equivariant Diffusioncited by 142
  2. 2024Space Group Constrained Crystal Generationcited by 71
  3. 2023Scalable Diffusion for Materials Generationcited by 52
  4. 2024FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributionscited by 52
  5. 2024LLMatDesign: Autonomous Materials Discovery with Large Language Modelscited by 41
  6. 2024Space Group Informed Transformer for Crystalline Materials Generationcited by 40
  7. 2022Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraintscited by 30
  8. 2025Hypothesis Generation for Materials Discovery and Design Using Goal-Driven and Constraint-Guided LLM Agentscited by 30
  9. 2023Crystal-GFN: sampling crystals with desirable properties and constraintscited by 29
  10. 2023Towards Symmetry-Aware Generation of Periodic Materialscited by 25
  11. 2025LeMat-GenBench: A Unified Evaluation Framework for Crystal Generative Modelscited by 20
  12. 2022Data-driven discovery of novel 2D materials by deep generative modelscited by 18
38 more
  1. 2023Diffusion probabilistic models enhance variational autoencoder for crystal structure generative modelingcited by 17
  2. 2024MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworkscited by 15
  3. 2025A Periodic Bayesian Flow for Material Generationcited by 15
  4. 2022Capturing long-range interaction with reciprocal space neural networkcited by 13
  5. 2023Equivariant Message Passing Neural Network for Crystal Material Discoverycited by 10
  6. 2023Vector Field Oriented Diffusion Model for Crystal Material Generationcited by 9
  7. 2025Materials Generation in the Era of Artificial Intelligence: A Comprehensive Surveycited by 9
  8. 2025Kinetic Langevin Diffusion for Crystalline Materials Generationcited by 9
  9. 2025Establishing baselines for generative discovery of inorganic crystalscited by 8
  10. 2025Equivariant Diffusion for Crystal Structure Predictioncited by 8
  11. 2024Generative Inverse Design of Crystal Structures via Diffusion Models with Transformerscited by 7
  12. 2025Unified Cross-Scale 3D Generation and Understanding via Autoregressive Modelingcited by 5
  13. 2025Continuous SUN (Stable, Unique, and Novel) Metric for Generative Modeling of Inorganic Crystalscited by 5
  14. 2022Atomic structure generation from reconstructing structural fingerprintscited by 4
  15. 2024Generative Design of Crystal Structures by Point Cloud Representations and Diffusion Modelcited by 4
  16. 2024Crystal-LSBO: Automated Design of De Novo Crystals with Latent Space Bayesian Optimizationcited by 4
  17. 2025Generative Models for Crystalline Materialscited by 3
  18. 20253DID: Direct 3D Inverse Design for Aerodynamics with Physics-Aware Optimizationcited by 3
  19. 2023Evaluating the diversity and utility of materials proposed by generative modelscited by 2
  20. 2023Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cellscited by 2
  21. 2022Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineeringcited by 1
  22. 2023Closed-loop Error Correction Learning Accelerates Experimental Discovery of Thermoelectric Materialscited by 1
  23. 2023Global optimization in the discrete and variable-dimension conformational space: The case of crystal with the strongest atomic cohesioncited by 1
  24. 2024A Generation Framework with Strict Constraints for Crystal Materials Designcited by 1
  25. 2024Classifier-pruned Bayesian optimization for particle accelerator tuning: Exploring temporally structured manifold of 6D beam phase spacecited by 1
  26. 2025ContinuouSP: Generative Model for Crystal Structure Prediction with Invariance and Continuitycited by 1
  27. 2025Autonomous Inorganic Materials Discovery via Multi-Agent Physics-Aware Scientific Reasoningcited by 1
  28. 2026MEIDNet: Multimodal generative AI framework for inverse materials designcited by 1
  29. 2026Fourier Transformers for Latent Crystallographic Diffusion and Generative Modelingcited by 1
  30. 2026Latent Diffusion Pretraining for Crystal Property Predictioncited by 1
  31. 2024A Straightforward Gradient-Based Approach for High-Tc Superconductor Design: Leveraging Domain Knowledge via Adaptive Constraintscited by 0
  32. 2024Adaptable Method for Crystal Design across Diverse Constraints and Objectives with Pretrained Property Predictorscited by 0
  33. 2024Unleashing the power of novel conditional generative approaches for new materials discoverycited by 0
  34. 2025Design Topological Materials by Reinforcement Fine-Tuned Generative Modelcited by 0
  35. 2025Synthesizability Prediction of Crystalline Structures with Structure-Aware Feature Learning and Uncertainty Quantificationcited by 0
  36. 2026A large-scale nanocrystal database with aligned synthesis and properties enabling generative inverse designcited by 0
  37. 2026Inverse Design of Inorganic Compounds with Generative AIcited by 0
  38. 2026A Padding Method for Enhanced Encoding of Inorganic Structures with Varying Chemical Compositionscited by 0

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